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EGFR-Binding Peptides: From Computational Design towards Tumor-Targeting of Adeno-Associated Virus Capsids.

ABSTRACT: The epidermal growth factor receptor (EGFR) plays a central role in the progression of many solid tumors. We used this validated target to analyze the de novo design of EGFR-binding peptides and their application for the delivery of complex payloads via rational design of a viral vector. Peptides were computationally designed to interact with the EGFR dimerization interface. Two new peptides and a reference (EDA peptide) were chemically synthesized, and their binding ability characterized. Presentation of these peptides in each of the 60 capsid proteins of recombinant adeno-associated viruses (rAAV) via a genetic based loop insertion enabled targeting of EGFR overexpressing tumor cell lines. Furthermore, tissue distribution and tumor xenograft specificity were analyzed with systemic injection in chicken egg chorioallantoic membrane (CAM) assays. Complex correlations between the targeting of the synthetic peptides and the viral vectors to cells and in ovo were observed. Overall, these data demonstrate the potential of computational design in combination with rational capsid modification for viral vector targeting opening new avenues for viral vector delivery and specifically suicide gene therapy.

SUBMITTER: Feiner RC

PROVIDER: S-EPMC7765298 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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EGFR-Binding Peptides: From Computational Design towards Tumor-Targeting of Adeno-Associated Virus Capsids.

Feiner Rebecca C RC Kemker Isabell I Krutzke Lea L Allmendinger Ellen E Mandell Daniel J DJ Sewald Norbert N Kochanek Stefan S Müller Kristian M KM

International journal of molecular sciences 20201215 24

The epidermal growth factor receptor (EGFR) plays a central role in the progression of many solid tumors. We used this validated target to analyze the de novo design of EGFR-binding peptides and their application for the delivery of complex payloads via rational design of a viral vector. Peptides were computationally designed to interact with the EGFR dimerization interface. Two new peptides and a reference (EDA peptide) were chemically synthesized, and their binding ability characterized. Prese ...[more]

PMID: 33333826

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Project description:Anti-cancer peptides (ACPs) are short peptides known for their ability to inhibit tumor cell proliferation, migration, and the formation of tumor blood vessels. In this study, we designed ACPs to target receptors often overexpressed in cancer using a systematic in silico approach. Three target receptors (CXCR1, DcR3, and OPG) were selected for their significant roles in cancer pathogenesis and tumor cell proliferation. Our peptide design strategy involved identifying interacting residues (IR) of these receptors, with their natural ligands serving as a reference for designing peptides specific to each receptor. The natural ligands of these receptors, including IL8 for CXCR1, TL1A for DcR3, and RANKL for OPG, were identified from the literature. Using the identified interacting residues (IR), we generated a peptide library through simple permutation and predicted the structure of each peptide. All peptides were analyzed using the web-based prediction server for Anticancer peptides, AntiCP. Docking simulations were then conducted to analyze the binding efficiencies of peptides with their respective target receptors, using VEGA ZZ and Chimera for interaction analysis. Our analysis identified HPKFIKELR as the interacting residues (IR) of CXCR-IL8. For DcR3, we utilized three domains from TL1A (TDSYPEP, TKEDKTF, LGLAFTK) as templates, along with two regions (SIKIPSS and PDQDATYP) from RANKL, to generate a library of peptide analogs. Subsequently, peptides for each receptor were shortlisted based on their predicted anticancer properties as determined by AntiCP and were subjected to docking analysis. After docking, peptides that exhibited the least binding energy were further analyzed for their detailed interaction with their respective receptors. Among these, peptides C9 (HPKFELY) and C7 (HPKFEWL) for CXCR1, peptides D6 (ADSYPQP) and D18 (AFSYPFP) for DcR3, and peptides P19 (PDTYPQDP) and p16 (PDQDATYP) for OPG, demonstrated the highest affinity and stronger interactions compared to the other peptides. Although in silico predictions indicated a favorable binding affinity of the designed peptides with target receptors, further experimental validation is essential to confirm their binding affinity, stability and pharmacokinetic characteristics.

Project description:The human epidermal growth factor receptor (EGFR) is closely related to several cancer-promoting processes and overexpressed on a variety of tumor types, rendering it an important target structure for the imaging and therapy of several malignancies. To date, approaches to develop peptidic radioligands able to specifically address and visualize EGFR-positive tumors have been of limited success. Most of the attempts were based on the lead GE11, as this peptide was previously described to be a highly potent EGFR-specific agent. However, since it has recently been shown that GE11 exhibits an insufficient affinity to the EGFR in monomeric form to be suitable as a basis for the development of tracers based on it, in the present work we investigated which other peptides might be suitable as lead structures for the development of EGFR-specific peptidic radiotracers. For this purpose, we developed 68Ga-labeled radioligands based on the peptides D4, P1, P2, CPP, QRH, EGBP and Pep11, having been described before as EGFR-specific. In addition, we also tested three truncated versions of the endogenous EGFR ligand hEGF (human epidermal growth factor) with respect to their ability to specifically target the EGFR with high affinity. Therefore, chelator-modified labeling precursors of the mentioned peptides were synthesized, radiolabeled with 68Ga and the obtained radioligands were evaluated for their hydrophilicity/lipophilicity, stability against degradation by human serum peptidases, in vitro tumor cell uptake, and receptor affinity in competitive displacement experiments on EGFR-positive A431 cells. Although all NODA-GA-modified (NODA-GA: (1,4,7-triazacyclononane-4,7-diyl)diacetic acid-1-glutaric acid) labeling precursors could be obtained more or less efficient in yields between 5 and 74%, the 68Ga-radiolabeling proved to be unsuccessful for two of the three truncated versions of hEGF ([68Ga]Ga-8 and [68Ga]Ga-9), producing several side-products. For the other agents [68Ga]Ga-1-[68Ga]Ga-7, [68Ga]Ga-10 and [68Ga]Ga-11, high radiochemical yields and purities of ≥98% and molar activities of up to 114 GBq/µmol were obtained. In the assay investigating the radiopeptide susceptibilities against serum peptidase degradation, the EGBP-based agent demonstrated a limited stability with a half-life of only 66.4 ± 3.0 min, whereas the other tracers showed considerably higher stabilities of up to an 8000 min half-life. Finally, all radiotracer candidates were evaluated in terms of tumor cell internalization and receptor binding potential on EGFR-positive A431 cell. In these experiments, all developed agents failed to show an EGFR-specific tumor cell uptake or a relevant EGFR-affinity. By contrast, the positive controls tested under identical conditions, [125I]I-hEGF and hEGF demonstrated the expected high EGFR-specific tumor cell uptake (33.6% after 1 h, being reduced to 1.9% under blocking conditions) and affinity (IC50 value of 15.2 ± 3.3 nM). Thus, these results indicate that none of the previously described peptidic agents developed for EGFR targeting appears to be a reasonable choice as a lead structure for the development of radiopeptides for targeting of EGFR-positive tumors. Likewise, the tested truncated variants of the endogenous hEGF do not seem to be promising alternatives for this purpose.

Project description:BackgroundMost methods available to predict protein epitopes are sequence based. There is a need for methods using 3D information for prediction of discontinuous epitopes and derived immunogenic peptides.ResultsPEPOP uses the 3D coordinates of a protein both to predict clusters of surface accessible segments that might correspond to epitopes and to design peptides to be used to raise antibodies that target the cognate antigen at specific sites. To verify the ability of PEPOP to identify epitopes, 13 crystallographically defined epitopes were compared with PEPOP clusters: specificity ranged from 0.75 to 1.00, sensitivity from 0.33 to 1.00, and the positive predictive value from 0.19 to 0.89. Comparison of these results with those obtained with two other prediction algorithms showed comparable specificity and slightly better sensitivity and PPV. To prove the capacity of PEPOP to predict immunogenic peptides that induce protein cross-reactive antibodies, several peptides were designed from the 3D structure of model antigens (IA-2, TPO, and IL8) and chemically synthesized. The reactivity of the resulting anti-peptides antibodies with the cognate antigens was measured. In 80% of the cases (four out of five peptides), the flanking protein sequence process (sequence-based) of PEPOP successfully proposed peptides that elicited antibodies cross-reacting with the parent proteins. Polyclonal antibodies raised against peptides designed from amino acids which are spatially close in the protein, but separated in the sequence, could also be obtained, although they were much less reactive. The capacity of PEPOP to design immunogenic peptides that induce antibodies suitable for a sandwich capture assay was also demonstrated.ConclusionPEPOP has the potential to guide experimentalists that want to localize an epitope or design immunogenic peptides for raising antibodies which target proteins at specific sites. More successful predictions of immunogenic peptides were obtained when a peptide was continuous as compared with peptides corresponding to discontinuous epitopes. PEPOP is available for use at http://diagtools.sysdiag.cnrs.fr/PEPOP/.

Dataset Information

EGFR-Binding Peptides: From Computational Design towards Tumor-Targeting of Adeno-Associated Virus Capsids.

Publications

EGFR-Binding Peptides: From Computational Design towards Tumor-Targeting of Adeno-Associated Virus Capsids.

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